Nistor Grozavu

From variable weighting to cluster characterization in topographic unsupervised learning

We introduce a new learning approach, which provides simultaneously Self-Organizing Map (SOM) and local weight vector for each cluster. The proposed approach is computationally simple, and learns a different features vector weights for each cell (relevance vector). Based on the Self-Organizing Map approach, we present two new simultaneously clustering and weighting algorithms: local weighting observation lwo-SOM and local weighting distance lwd-SOM. Both algorithms achieve the same goal by minimizing different cost functions. After learning phase, a selection method with weight vectors is used to prune the irrelevant variables and thus we can characterize the clusters. We illustrate the performance of the proposed approach using different data sets. A number of synthetic and real data are experimented on to show the benefits of the proposed local weighting using self-organizing models.

COLLABORATIVE CLUSTERING USING PROTOTYPE-BASED TECHNIQUES

The aim of collaborative clustering is to reveal the common structure of data distributed on different sites. In this paper, we present a formalism of topological collaborative clustering using prototype-based clustering techniques; in particular we formulate our approach using Kohonens Self-Organizing Maps. Maps representing different sites could collaborate without recourse to the original data, preserving their privacy. We present two different approaches of collaborative clustering: horizontal and vertical. The strength of collaboration (confidence exchange) between each pair of datasets is determined by a parameter, we call coefficient of collaboration, to be estimated iteratively during the collaboration phase using a gradient-based optimization, for both the approaches. The proposed approaches have been validated on several datasets and experimental results have shown very promising performance.

Learning confidence exchange in Collaborative Clustering

The aim of collaborative clustering is to reveal the common structure of data which are distributed on different sites. The topological collaborative clustering (based on Kohonen Self-Organizing Maps) allows to take into account other maps without recourse to the data in an unsupervised learning. In this paper, the approach is presented in the case of SOM and it is valid for all prototypes based classifications methods. The strength of the collaboration between each pair of datasets is determined by a fixed parameter for the both, vertical and horizontal topological collaborative clustering. In this study, learning the confidence exchange is presented for the both topological collaborative clustering approaches by using the topological knowledge. The gradient based optimization is used to set the value of the confidence parameter for each collaboration. The paper presents the formalism of the approach and its validation. The proposed approach has been validated on several datasets and experimental results have shown very promising performance.

Collaborative multi-view clustering

The purpose of this article is to introduce a new collaborative multi-view clustering approach based on a probabilistic model. The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple data sites by applying clustering techniques. The strength of the collaboration between each pair of data repositories is determined by a fixed parameter. Previous works considered deterministic techniques such as Fuzzy C-Means (FCM) and Self-Organizing Maps (SOM). In this paper, we present a new approach for the collaborative clustering using a generative model, which is the Generative Topographic Mappings (GTM). Maps representing different sites could collaborate without recourse to the original data, preserving their privacy. We present the approach for multi-view collaboration using GTM, where data sets have the same observations but presented in different feature space; i.e. different dimensions. The proposed approach has been validated on several data sets, and experimental results have shown very promising performance.

Collaborative clustering with heterogeneous algorithms

The aim of collaborative clustering is to reveal the common underlying structures found by different algorithms while analyzing data. The fundamental concept of collaboration is that the clustering algorithms operate locally but collaborate by exchanging information about the local structures found by each algorithm. In this framework, the one purpose of this article is to introduce a new method which allows to reinforce the clustering process by exchanging information between several results acquired by different clustering algorithms. The originality of our proposed approach is that the collaboration step can use clustering results obtained from any type of algorithm during the local phase. This article gives the theoretical foundations of our approach as well as some experimental results. The proposed approach has been validated on several data sets and the results have shown to be very competitive.

Collaborative Clustering: How to Select the Optimal Collaborators?

The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple data sites by applying clustering techniques. The idea of Collaborative Clustering is that each collaborator share some information about the segmentation (structure) of its local data and improve its own clustering with the information provided by the other collaborators. This paper analyses the impact of the Quality of the potential Collaborators to the quality of the collaboration for a Topological Collaborative Clustering Algorithm based on the learning of a Self-Organizing Map. Experimental analysis on four real vector data-sets showed that the diversity between collaborators impact the quality of the collaboration. We also showed that the internal indexes of quality are a good estimator of the increase of quality due to the collaboration.

Entropy based probabilistic collaborative clustering

Abstract Unsupervised machine learning approaches involving several clustering algorithms working together to tackle difficult data sets are a recent area of research with a large number of applications such as clustering of distributed data, multi-expert clustering, multi-scale clustering analysis or multi-view clustering. Most of these frameworks can be regrouped under the umbrella of collaborative clustering, the aim of which is to reveal the common underlying structures found by the different algorithms while analyzing the data. Within this context, the purpose of this article is to propose a collaborative framework lifting the limitations of many of the previously proposed methods: Our proposed collaborative learning method makes possible for a wide range of clustering algorithms from different families to work together based solely on their clustering solutions, thus lifting previous limitation requiring identical prototypes between the different collaborators. Our proposed framework uses a variational EM as its theoretical basis for the collaboration process and can be applied to any of the previously mentioned collaborative contexts. In this article, we give the main ideas and theoretical foundations of our method, and we demonstrate its effectiveness in a series of experiments on real data sets as well as data sets from the literature.

Diversity analysis in collaborative clustering

The aim of collaborative clustering is to reveal the common structure of data which are distributed on different sites. The topological collaborative clustering, based on Self-Organizing Maps (SOM) is an unsupervised learning method which is able to use the output of other SOMs from other sites during the learning. This paper investigates the impact of the diversity between collaborators on the collaborations quality and presents a study of different diversity indexes for collaborative clustering. Based on experiments on artificial and real datasets, we demonstrated that the quality and the diversity of the collaboration can have an important impact on the quality of the collaboration and that not all diversity indexes are relevant for this task.

Collaborative generative topographic mapping

The aim of collaborative clustering is to reveal the common structure of data distributed on different sites. In this paper, we present a new approach for the topological collaborative clustering using a generative model, which is the Generative Topographic Mappings (GTM). In this case, maps representing different sites could collaborate without recourse to the original data, preserving their privacy. Depending ont the data structure, there are three different ways of collaborative clustering: horizontal, vertical and hybrid. In this study we introduce the Collaborative GTM for the vertical collaboration. The article presents the formalism of the approach and its validation. The proposed approach has been validated on several datasets and experimental results have shown very promising performance.

From Horizontal to Vertical Collaborative Clustering using Generative Topographic Maps

Collaborative clustering is a recent field of Machine Learning that shows similarities with both ensemble learning and transfer learning. Using a two-step approach where different clustering algorithms first process data individually and then exchange their information and results with a goal of mutual improvement, collaborative clustering has shown promising performances when trying to have several algorithms working on the same data. However the field is still lagging behind when it comes to transfer learning where several algorithms are working on different data with similar clusters and the same features. In this article, we propose an original method where we combine the topological structure of the Generative Topographic Mapping (GTM) algorithm and take advantage of it to transfer information between collaborating algorithms working on different data sets featuring similar distributions. The proposed approach has been validated on several data sets, and the experimental results have shown very promising performances.